Activities of Daily Work Monitoring and Reporting System

Abstract

A user-wearable electronic device includes a housing configured to be worn or embedded in a device worn by an employee, one or more sensors disposed in the housing, including a first sensor to sense motion of the employee and produce raw activities of daily work (ADW) data. One or more processors in the electronic device or an intermediary device generate, for time periods in a sequence of successive time periods, ADW identification information by processing the raw ADW data using one or more neural networks pre-trained to recognize a predefined set of ADWs. Each pre-trained neural network includes a plurality of neural network layers, including at least one layer that includes a recurrent neural network. Reports that include ADW information corresponding to the generated ADW identification information for one or more time periods in the sequence of time periods are transmitted to a monitoring system.

What is claimed is: 
     
         1 . A user-wearable electronic device, for monitoring user activities of daily work (ADW), comprising:
 a housing configured to be worn by or embedded in a device worn by a user;   one or more sensors disposed in the housing, including a first sensor to sense motion of the user and produce raw ADW data;   one or more processors, disposed in the housing and coupled to the one or more sensors, configured to:
 for each time period in a sequence of successive time periods,
 generate ADW identification information for the time period by processing the raw ADW data produced by the first sensor using one or more neural networks pre-trained to recognize a predefined set of ADWs, the pre-trained one or more neural networks each including a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network, wherein an output of the one or more neural networks for each time period corresponds to the generated ADW identification information for the time period; and 
 
   a communication interface, disposed in the housing and coupled to at least one of the one or more processors, to transmit one or more reports corresponding to the user, wherein a respective report corresponding to the user includes ADW information corresponding to the generated ADW identification information for one or more time periods in the sequence of time periods.   
     
     
         2 . The user-wearable device  claim 1 , wherein the predefined set of ADWs includes three or more activities of daily work and the generated ADW identification information for a respective time period in the sequence of successive time periods includes classification information identifying a dominant activity for the respective time period, wherein the dominant activity is one of the ADWs in the predefined set of ADWs. 
     
     
         3 . The user-wearable device of  claim 2 , wherein a respective report includes the classification information for a time period corresponding to the respective report in accordance with the identified dominant activity for a corresponding set of the time periods in the sequence of time periods. 
     
     
         4 . The user-wearable device of  claim 1 , wherein the predefined set of ADWs includes three or more activities of daily work and the generated ADW identification information for a respective time period in the sequence of successive time periods includes classification information identifying which, if any, of the ADWs in the predefined set of ADWs are consistent with the raw ADW data produced by the first sensor. 
     
     
         5 . The user-wearable device of  claim 4 , wherein
 in accordance with the classification information identifying an ADW which is consistent with the raw ADW data, a respective report includes the classification information identifying the ADW; and   in accordance with the classification information not identifying any ADWs which are consistent with the raw ADW data, a respective report includes an indication of no ADWs for the respective time period.   
     
     
         6 . The user-wearable device of  claim 1 , wherein the predefined set of ADWs includes three or more activities of daily work and the generated ADW identification information for a respective time period in the sequence of successive time periods includes one or more count values associated with one or more of the ADWs in the predefined set of ADWs. 
     
     
         7 . The user-wearable device of  claim 6 , wherein a respective report includes the one or more count values associated with the one or more ADWs. 
     
     
         8 . The user-wearable device of  claim 1 , wherein the respective report further includes raw ADW data sensed during the respective time period. 
     
     
         9 . The user-wearable device of  claim 1 , wherein the predefined set of ADWs includes generic activities which are common to a plurality of job categories, and includes four or more generic activities from the group consisting of:
 operating a vehicle;   being transported in a vehicle;   ambulating within a defined work space;   ambulating outside a defined work space;   ambulating;   interacting with another person;   interacting with a computer or electronic device; and   inactivity.   
     
     
         10 . The user-wearable device of  claim 1 , wherein the predefined set of ADWs includes job-specific activities which are specific to a job category selected from the group consisting of:
 retail;   stocking;   customer service;   restaurant service;   cleaning;   manufacturing;   security;   delivery;   healthcare;   landscaping; and   farming.   
     
     
         11 . The user-wearable device of  claim 10 , wherein the predefined set of ADWs is specific to the healthcare job category, and includes two or more activities from the group consisting of:
 attending to a patient;   performing a specific procedure;   washing hands; and   charting.   
     
     
         12 . The user-wearable device of  claim 1 , wherein the first sensor comprises an accelerometer, an orientation sensor, motion sensor, or gyroscopic sensor. 
     
     
         13 . The user-wearable device of  claim 1 , further comprising a location or proximity sensor disposed in or on the housing;
 wherein the one or more processors are further configured to:
 determine location information for the user based on data from the location or proximity sensor; and 
 generate at least a portion of the ADW identification information for the time period by processing the raw ADW data produced by the first sensor and the location information for the user using at least one of the one or more neural networks. 
   
     
     
         14 . The user-wearable device of  claim 13 , wherein the location or proximity sensor is configured to obtain or generate range or proximity information corresponding to a range or proximity to one or more beacons at known locations in an environment occupied by the user; and wherein at least one processor of the one or more processors is configured to determine the location information for the user based on the range or proximity information. 
     
     
         15 . The user-wearable device of  claim 1 , wherein the one or more processors is configured to generate the ADW identification information for a respective time period in the sequence of time periods by:
 generating a set of scores, including one or more scores for each ADW in the predefined set of ADWs;   in accordance with the generated set of scores, determining a dominant activity for the respective time period, wherein the dominant activity is one of the ADWs in the predefined set of ADWs;   in accordance with a determination that the one or more scores for the dominant activity for the respective time period meets predefined criteria, including in the generated ADW identification information for the respective time period information identifying the dominant activity for the respective time period.   
     
     
         16 . The user-wearable device of  claim 1 , wherein the predefined set of ADWs includes N distinct ADWs, where N is an integer greater than 2, and the ADW identification information generated by the one or more processors for the time period includes a vector having at least N elements, only one of which is set to a non-null value. 
     
     
         17 . The user-wearable device of  claim 1 , wherein
 the pre-trained one or more neural networks include a first neural network having a first configuration;   the communication interface is configured to receive an updated configuration for the first neural network; and   the one or more processors are further configured to reconfigure the first neural network with the updated configuration, and to thereafter generate ADW identification information for time periods subsequent to the reconfiguring of the first neural network, using the first neural network configured using the updated configuration.   
     
     
         18 . The user-wearable device of  claim 1 , wherein the communication interface comprises a wireless transceiver, the housing has a length no greater than 7 cm, a height no greater than 3 cm, and a thickness of 2-3 mm, and the housing and all components within the housing have a total weight no greater than 120 grams. 
     
     
         19 . The user-wearable device of  claim 1 , wherein the successive time periods each have a duration of no more than 30 seconds, and the predefined times at which the transmitter transmits reports for the user occur at intervals of no less than 5 minutes. 
     
     
         20 . The user-wearable device of  claim 1 , wherein the communication interface is configured to transmit a report at a predefined event selected from the group consisting of:
 a detected violation;   a crossed threshold of time during which an activity has been performed;   a crossed threshold of time during which inactivity has been detected;   a crossed threshold of activity counts; and   a powering event during which the user-wearable device is attached to or placed in a vicinity of a power source.   
     
     
         21 . The user-wearable device of  claim 1 , wherein the one or more processors are configured to receive raw ADW data from the first sensor at a rate of no less than 10 samples per second, in accordance with a sampling period, and a ratio of the time period to the sampling period is no less than 100. 
     
     
         22 . The user-wearable device of  claim 1 , further comprising a rechargeable battery disposed within the housing, wherein the one or more processors are further configured to: perform a predefined set of tasks while the user-wearable device is determined to be connected to a power source for recharging the user-wearable device's battery, the predefined set of tasks including transmitting recorded information not transmitted when the user-wearable device is connected to a power source for recharging the user-wearable device's battery, and receiving update information for reconfiguring at least one aspect of the user-wearable device. 
     
     
         23 . An activities of daily work (ADW) monitoring system, comprising:
 one or more processors, configured to:
 collect raw ADW data produced by a first sensor; 
 for each time period in a sequence of successive time periods, generate ADW identification information for the time period by processing the raw ADW data produced by the first sensor using one or more neural networks pre-trained to recognize a predefined set of ADWs, the pre-trained one or more neural networks each including a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network, wherein an output of the one or more neural networks for each time period corresponds to the generated ADW identification information for the time period; and 
 transmit one or more reports corresponding to the user, wherein a respective report corresponding to the user includes ADW information corresponding to the generated ADW identification information for one or more time periods in the sequence of time periods. 
   
     
     
         24 . The ADW monitoring system of  claim 23 , wherein
 the system includes a user-wearable electronic device and an intermediary device configured to receive raw ADW from the user-wearable electronic device;   the first sensor is disposed in a housing, located in the user-wearable electronic device, the housing configured to be worn by or embedded in a device worn by a user; and   the ADW identification information is generated by one or more processors in the intermediary device using the one or more neural networks pre-trained to recognize a predefined set of ADWs.